The development of self-driving cars has progressed significantly due to the expansion in perception, motion planning and control, and/or emerging sensing technologies. To achieve autonomous navigation, accurate localization may be used. While a Global Positioning System (“GPS”) may be used, it may suffer from multipath effects in urban environments. Alternatives may be used for localization in GPS-challenged environments.
Localization may match sensor observations against an a priori known map. Maps may be generated by human surveying or robotic mapping using different sensors. Cameras and light detection and ranging (“LiDAR”) are two common perception sensors. LiDAR may be used for mapping because it generally provides accurate range measurements. A common approach may be to use LiDAR in the mapping process as well as localization. However, the cost of LiDAR may be prohibitively high for wide ranging applications. On the other hand, cameras are low-cost and lightweight, but visual mapping is challenging due, in part, to the lack of direct range measurement. The challenge becomes matching measurements against maps that may be constructed using different sensing modalities.
In view of the foregoing, there may be a need for ways to more accurately implement localization for autonomous vehicles. Further advantages and novel features will become apparent from the disclosure provided below.